Outcome Comparison of Totally Implantable Venous Access Device Insertions Between Surgeons and Radiologists in Australia
Bibliographic record
Abstract
Background The need for chemotherapy treatment is increasing with the growing incidence of cancer worldwide. The insertion of totally implantable venous access devices (TIVADs) is commonly performed by surgeons and radiologists, but the procedures are not without complications. The primary outcome of this review outlines TIVAD insertion success and complication rates between general surgeons and radiologists. The secondary goal of this study is to help identify areas for improvement and consideration when performing TIVAD insertion. Methodology This was a descriptive, three-year, retrospective multicentre study of oncological patients who underwent TIVAD insertion by either general surgeons or radiologists at two peripheral Brisbane hospitals. Results Surgeons performed 61 percutaneous subclavian vein cannulations, 29 ultrasound-guided internal jugular veins, and seven open cephalic veins cut-down TIVAD insertions (n=97). Overall surgical success was 81.4%, with the internal jugular (89.7%) having the highest success rate followed by the open cut-down (85.7) and subclavian approaches (77.0%). The overall surgical complication rate was 16.4%, with five pneumothorax, five port malfunctions, three haemorrhages, two infections, one thrombus, and one mediastinal injury. Each pneumothorax was associated with subclavian cannulation attempts. Two haemorrhages were associated with both open cephalic and subclavian attempts. Radiologists performed 248 ultrasound-guided internal jugular vein TIVAD insertions (n=248) with 247 successful first attempts (99.5%). Within the radiology group, there was an overall complication rate of 15.3% with 22 infections, 14 port malfunctions, one haemorrhage, and 1 mediastinal injury. Conclusion Ultrasound-guided internal jugular vein TIVAD insertion had the highest first attempt success rate in both the surgical and radiology groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".